{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Warning imports\n",
    "from IPython.display import HTML\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Standard imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "\n",
    "# Model imports\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "from sklearn import ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "FOLDS = 5\n",
    "\n",
    "skf = model_selection.StratifiedKFold(n_splits=FOLDS, shuffle=True, random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test.csv  Train.csv\r\n"
     ]
    }
   ],
   "source": [
    "# No need to run this cell\n",
    "\n",
    "!mkdir data\n",
    "!mv Train.csv Test.csv data\n",
    "!ls data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'data/'\n",
    "\n",
    "train = pd.read_csv(f'{path}Train.csv')\n",
    "test = pd.read_csv(f'{path}Test.csv')\n",
    "submissions = pd.read_excel('subs/Sample_Submission.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Numerical and Categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = list(train.columns[2:10])\n",
    "categorical_cols = [i for i in train.drop('class', axis=1).columns if i not in num_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking target distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.653166\n",
       "2    0.346834\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['class'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Seems like there is a slight **imbalance** in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical columns distribution in training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2304 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "fcols = 3\n",
    "frows = len(num_cols)\n",
    "plt.figure(figsize=(5*fcols,4*frows));\n",
    "\n",
    "i=0\n",
    "for col in num_cols:\n",
    "    i+=1\n",
    "    ax=plt.subplot(frows,fcols,i);\n",
    "    sns.distplot(train[col].dropna() , fit=stats.norm, kde=False);\n",
    "    sns.distplot(test[col].dropna() , fit=stats.norm, kde=False);\n",
    "    plt.title('skew='+'{:.4f}'.format(stats.skew(train[col])))\n",
    "    plt.xlabel(col);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Numerical columns seem to be equally distributed among training and testing set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical column distributions in training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([train, test], ignore_index=True)\n",
    "data['is_train'] = np.where(data['class'].isnull(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2016 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fcols = 3\n",
    "frows = len(categorical_cols)\n",
    "plt.figure(figsize=(12,4*frows));\n",
    "\n",
    "for i,col in enumerate(categorical_cols):\n",
    "    plt.subplot(frows,fcols,i+1);\n",
    "    sns.countplot(data[col], hue=data['is_train']);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Categorical columns too seem to be equally distributed in training and testing sets\n",
    "- `x_component_x` columns seem to have very low instances in positive class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Relationship between independant categorical and target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2016 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fcols = 3\n",
    "frows = len(categorical_cols)\n",
    "plt.figure(figsize=(12,4*frows));\n",
    "\n",
    "for i,col in enumerate(categorical_cols):\n",
    "    plt.subplot(frows,fcols,i+1);\n",
    "    sns.countplot(train[col], hue=train['class']);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Well, it seems like we found a **RULE**, wherever `x_component_x` == 1, `class` is always == 1\n",
    "- Also during EDA it was observed that `grade_A_component_x` and `x_component_x` columns were one-hot encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('class', axis=1)\n",
    "y = train['class'].values\n",
    "Xt = test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse the one-hot encoding on `x_compnent_x` columns and convert it into ordinals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reverse_ohe(row):\n",
    "    for c in x_component:\n",
    "        if row[c]==1:\n",
    "            return c\n",
    "\n",
    "        \n",
    "x_component = ['x_component_1', 'x_component_2', 'x_component_3', 'x_component_4', 'x_component_5']\n",
    "\n",
    "X['x_component'] = X[x_component].apply(reverse_ohe, axis=1)\n",
    "Xt['x_component'] = Xt[x_component].apply(reverse_ohe, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_mapper = {'None' : 0, 'x_component_1' : 1, 'x_component_2' : 2,'x_component_3' : 3, 'x_component_4' : 4, 'x_component_5' : 5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['x_component'] = X['x_component'].map(x_mapper).fillna(0).astype('int')\n",
    "Xt['x_component'] = Xt['x_component'].map(x_mapper).fillna(0).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frequency encoding `x_component` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequency_encoding(column, df, df_test=None):\n",
    "    frequencies = df[column].value_counts().reset_index()\n",
    "    df_values = df[[column]].merge(frequencies, how='left', \n",
    "                                   left_on=column, right_on='index').iloc[:,-1].values\n",
    "    if df_test is not None:\n",
    "        df_test_values = df_test[[column]].merge(frequencies, how='left', \n",
    "                                                 left_on=column, right_on='index').fillna(1).iloc[:,-1].values\n",
    "    else:\n",
    "        df_test_values = None\n",
    "    return df_values, df_test_values\n",
    "\n",
    "for column in ['x_component', 'grade_A_Component_1']:\n",
    "    train_values, test_values = frequency_encoding(column, X, Xt)\n",
    "    X[column+'_counts'] = train_values\n",
    "    Xt[column+'_counts'] = test_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['grade_A_Component_1', 'x_component','pixel_area'], axis=1, inplace=True)\n",
    "Xt.drop(['grade_A_Component_1', 'x_component','pixel_area'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['xmin_sub_xmax'] = abs(X['xmin'] - X['xmax'])\n",
    "X['thickness_sub_max_luminosity'] = abs(X['thickness'] - X['max_luminosity'])\n",
    "\n",
    "X['thickness_div_max_luminosity'] = X['thickness'] / X['max_luminosity']\n",
    "X['xmin_div_xmax'] = X['xmin'] / X['xmax']\n",
    "X['xmax_div_thickness'] = X['xmax'] / X['thickness']\n",
    "X['ymin_div_thickness'] = X['ymin'] / X['thickness']\n",
    "\n",
    "X['max_luminosity_mul_thickness'] = X['max_luminosity'] * X['thickness']\n",
    "X['xmax_mul_thickness'] = X['xmax'] * X['thickness']\n",
    "X['ymin_mul_thickness'] = X['ymin'] * X['thickness']\n",
    "X['ymax_mul_thickness'] = X['ymax'] * X['thickness']\n",
    "X['logarea_mul_thickness'] = X['log_area'] * X['thickness']\n",
    "\n",
    "\n",
    "\n",
    "Xt['xmin_sub_xmax'] = abs(Xt['xmin'] - Xt['xmax'])\n",
    "Xt['thickness_sub_max_luminosity'] = abs(Xt['thickness'] - Xt['max_luminosity'])\n",
    "\n",
    "Xt['thickness_div_max_luminosity'] = Xt['thickness'] / Xt['max_luminosity']\n",
    "Xt['xmin_div_xmax'] = Xt['xmin'] / Xt['xmax']\n",
    "Xt['xmax_div_thickness'] = Xt['xmax'] / Xt['thickness']\n",
    "Xt['ymin_div_thickness'] = Xt['ymin'] / Xt['thickness']\n",
    "\n",
    "Xt['max_luminosity_mul_thickness'] = Xt['max_luminosity'] * Xt['thickness']\n",
    "Xt['xmax_mul_thickness'] = Xt['xmax'] * Xt['thickness']\n",
    "Xt['ymin_mul_thickness'] = Xt['ymin'] * Xt['thickness']\n",
    "Xt['ymax_mul_thickness'] = Xt['ymax'] * Xt['thickness']\n",
    "Xt['logarea_mul_thickness'] = Xt['log_area'] * Xt['thickness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1358, 26), (583, 26))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, Xt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean CV log loss: 0.2617 and std Dev. is 0.02 \n",
      "\n",
      "CPU times: user 12.9 s, sys: 222 ms, total: 13.1 s\n",
      "Wall time: 13.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "logloss = list()\n",
    "oof_etc = np.zeros((len(X),2))\n",
    "cv_test_preds_etc = np.zeros((len(Xt),2))\n",
    "\n",
    "for train_idx, test_idx in skf.split(X, y):\n",
    "    X_train, y_train = X.iloc[train_idx, :], y[train_idx]\n",
    "    X_test, y_test = X.iloc[test_idx, :], y[test_idx]\n",
    "    \n",
    "    etc = ensemble.ExtraTreesClassifier(n_estimators=1000, random_state=SEED, class_weight='balanced')\n",
    "    etc.fit(X_train, y_train)\n",
    "    \n",
    "    preds = etc.predict_proba(X_test)\n",
    "    oof_etc[test_idx] = preds\n",
    "    \n",
    "#   CV test prediction\n",
    "    cv_test_preds_etc += etc.predict_proba(Xt) / FOLDS\n",
    "    \n",
    "    \n",
    "    logloss = np.append(logloss, metrics.log_loss(y_true=y_test, y_pred=preds))\n",
    "    \n",
    "print(f'Mean CV log loss: {np.mean(logloss):.4f} and std Dev. is {np.std(logloss):.2f} \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the rule found during EDA on train and test columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss: 0.2601\n"
     ]
    }
   ],
   "source": [
    "oof_etc = pd.DataFrame(oof_etc, columns=['1','2'])\n",
    "\n",
    "train_preds = pd.concat([train, oof_etc], axis=1)\n",
    "train_preds['x_component'] = train_preds['x_component_1'] + train_preds['x_component_2'] + train_preds['x_component_3'] + train_preds['x_component_4'] + train_preds['x_component_5']\n",
    "\n",
    "train_preds.loc[train_preds['x_component'] == 1, '1'] = 1.0\n",
    "train_preds.loc[train_preds['x_component'] == 1, '2'] = 0.0\n",
    "\n",
    "Lloss = metrics.log_loss(train_preds['class'], train_preds[['1','2']])\n",
    "print(f'Log Loss: {Lloss:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_test_preds_etc = pd.DataFrame(cv_test_preds_etc, columns=['1','2'])\n",
    "test_preds = pd.concat([test, cv_test_preds_etc], axis=1)\n",
    "\n",
    "test_preds['x_component'] = test_preds['x_component_1'] + test_preds['x_component_2'] + test_preds['x_component_3'] + test_preds['x_component_4'] + test_preds['x_component_5']\n",
    "\n",
    "test_preds.loc[test_preds['x_component'] == 1, '1'] = 1.0\n",
    "test_preds.loc[test_preds['x_component'] == 1, '2'] = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=etc_fe.xlsx>Download EXCEL file</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SUB_FILE_NAME = 'etc_fe.xlsx'\n",
    "sample_sub = test_preds[['1','2']]\n",
    "sample_sub.to_excel(SUB_FILE_NAME, index=False)\n",
    "\n",
    "def create_download_link(title = \"Download EXCEL file\", filename = \"data.xlsx\"):  \n",
    "    html = '<a href={filename}>{title}</a>'\n",
    "    html = html.format(title=title,filename=filename)\n",
    "    return HTML(html)\n",
    "\n",
    "\n",
    "create_download_link(filename = SUB_FILE_NAME)"
   ]
  }
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